{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1cf1f07d-9567-4419-b244-7003cf2d898d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 3. 6.]\n",
      "[7. 8.]\n",
      "X 网格坐标：\n",
      " [[7. 8.]\n",
      " [7. 8.]\n",
      " [7. 8.]]\n",
      "Y 网格坐标：\n",
      " [[0. 0.]\n",
      " [3. 3.]\n",
      " [6. 6.]]\n",
      "3\n",
      "3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 定义x和y的坐标范围\n",
    "x = np.linspace(0, 6, 3)  # x坐标，生成从0到5，步长为1的6个点\n",
    "y = np.linspace(7, 8, 2)  # y坐标，生成从0到5，步长为1的6个点\n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "# 使用meshgrid生成坐标网格\n",
    "X, Y = np.meshgrid(y,x)\n",
    "\n",
    "# 打印生成的网格\n",
    "print(\"X 网格坐标：\\n\", X)\n",
    "print(\"Y 网格坐标：\\n\", Y)\n",
    "print(len(X))\n",
    "print(len(Y))\n",
    "\n",
    "# 计算Z值\n",
    "Z = X**2 + Y**2\n",
    "\n",
    "# 绘制网格上的函数值\n",
    "plt.contourf(X, Y, Z)\n",
    "plt.colorbar()\n",
    "plt.title('Function Z = X^2 + Y^2')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cbd270c-c2a2-46ab-8620-cf724479218e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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